ergm.userterms-package: User-defined terms used in Exponential Family Random Graph...
In ergm.userterms: User-specified Terms for the statnet Suite of Packages
Description
Creating User-defined terms using this package
References
See Also
Examples
Description
This package contains template code for
user defined (change) statistics that can be used with the
statnet suite of packages (and ergm in particular).
To use this package the statnet
packages ergm and network are required.
As background,
statnet is a suite of software packages for statistical network analysis.
The packages implement recent advances in network modeling based on
exponential-family random graph models (ERGM). The components of the package
provide a comprehensive framework for ERGM-based network modeling: tools for
model estimation, for model evaluation, for model-based network simulation, and
for network visualization. This broad functionality is powered by a central
Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed
and robustness.
For detailed information on how to download and install the software,
go to the ergm website:
statnet.org.
A tutorial, support newsgroup, references and links to further
resources are provided there.
When publishing results obtained using this package the original authors
are to be cited as:
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau,
and Martina Morris. 2003
statnet: Software tools for the Statistical Modeling of Network Data
statnet.org.
We have invested a lot of time and effort in creating the
statnet suite of packages for use by other researchers.
Please cite it in all papers where it is used.
For complete citation information, use
citation(package="statnet").
Creating User-defined terms using this package
The statnet suite of packages allows the
user to explore a large number of potential models
for their network data. These can be seen by typing
help("ergm-terms",package="ergm") (once the package
ergm has been installed). For more information on
the models and terms see Morris, Handcock, and Hunter (2008).
The purpose of the package is to allow additional terms can
be coded up by users (you!) and be used at native speeds with
statnet. So the suite of packages can be extended with
minimal work by the user. In addition the core packages are
not altered and so the new packages benefit from improvements
to the core suite.
The process of creating new terms is explained in depth by the
document entitled
by Hunter, Goodreau, and Handcock (2010) that is
found in the inst/doc directory of this package.
In brief, to add a new term you need to (all file references are relative to the
package directory).:
1. Download the source version of this package. For example, in R use
download.packages("ergm.userterms", destdir=".", type="source")
where destdir is the directory to save the source
(e.g., ergm.userterms_3.0.tar.gz) in.
2. Unpack the source into a directory
(e.g., on Linux or the Mac
tar -vzxf ergm.userterms_3.0.tar.gz).
3. Optionally, rename the package name from
ergm.userterms to some something
more evocative of its use (e.g., myergm).
This can be done using a global change to the files in the
directory. Optionally, edit the DESCRIPTION file to reflect the
use of the package.
4. Edit R/InitErgmTerm.users.R to add
R InitErgmTerm functions for the new terms.
5. Edit src/changestats.users.c to add C
functions (like the example already in that file) to compute the
new change statistics.
6. Compile the package using the usual R
tools (e.g., R CMD build myergm and
R CMD INSTALL myergm).
7. Run it! It depends on ergm and network, of course.
See the example below.
References
Hunter DR, Goodreau SM, Handcock MS (2013).
ergm.userterms: A Template Package for
Extending statnet, Journal of Statistical
Software 52(2), 1-25,
URL http://www.jstatsoft.org/v52/i02/.
See Also
statnet, network, ergm, ergm-terms
Examples
1
2
3
4
Example output
Loading required package: network
network: Classes for Relational Data
Version 1.13.0.1 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Martina Morris, University of Washington
Skye Bender-deMoll, University of Washington
For citation information, type citation("network").
Type help("network-package") to get started.
Loading required package: ergm
ergm: version 3.9.4, created on 2018-08-15
Copyright (c) 2018, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Martina Morris, University of Washington
with contributions from
Li Wang
Kirk Li, University of Washington
Skye Bender-deMoll, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm").
NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
constriant which distorted the sampled distribution somewhat. In
addition, Sampson's Monks datasets had mislabeled vertices. See the
NEWS and the documentation for more details.
Loading required package: statnet.common
Attaching package: 'statnet.common'
The following objects are masked from 'package:ergm':
colMeans.mcmc.list, sweep.mcmc.list
The following object is masked from 'package:base':
order
ergm.userterms: version 3.1.1, created on 2013-04-26
Copyright (c) 2013, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm.userterms").
NOTE: If you use custom ERGM terms based on 'ergm.userterms' version
prior to 3.1, you will need to perform a one-time update of the package
boilerplate files (the files that you did not write or modify) from
'ergm.userterms' 3.1 or later. See help('eut-upgrade') for
instructions.
Warning messages:
1: replacing previous import 'statnet.common::sweep.mcmc.list' by 'ergm::sweep.mcmc.list' when loading 'ergm.userterms'
2: replacing previous import 'statnet.common::colMeans.mcmc.list' by 'ergm::colMeans.mcmc.list' when loading 'ergm.userterms'
mindegree2
97
Starting maximum pseudolikelihood estimation (MPLE):
Evaluating the predictor and response matrix.
Maximizing the pseudolikelihood.
Finished MPLE.
Stopping at the initial estimate.
Evaluating log-likelihood at the estimate.
==========================
Summary of model fit
==========================
Formula: faux.mesa.high ~ mindegree(2)
Iterations: NA
Maximum Pseudolikelihood Results:
Estimate Std. Error MCMC % z value Pr(>|z|)
mindegree2 -4.8709 0.1294 0 -37.64 <1e-04 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning: The standard errors are based on naive pseudolikelihood and are suspect.
Null Pseudo-deviance: 28987 on 20910 degrees of freedom
Residual Pseudo-deviance: 17022 on 20909 degrees of freedom
AIC: 17024 BIC: 17031 (Smaller is better.)
ergm.userterms documentation built on May 15, 2019, 9:03 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Creating User-defined terms using this package References See Also Examples
Description
This package contains template code for user defined (change) statistics that can be used with the statnet suite of packages (and ergm in particular). To use this package the statnet packages ergm and network are required.
As background,
statnet is a suite of software packages for statistical network analysis.
The packages implement recent advances in network modeling based on
exponential-family random graph models (ERGM). The components of the package
provide a comprehensive framework for ERGM-based network modeling: tools for
model estimation, for model evaluation, for model-based network simulation, and
for network visualization. This broad functionality is powered by a central
Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed
and robustness.
For detailed information on how to download and install the software,
go to the ergm website:
statnet.org.
A tutorial, support newsgroup, references and links to further
resources are provided there.
When publishing results obtained using this package the original authors are to be cited as:
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau,
and Martina Morris. 2003
statnet: Software tools for the Statistical Modeling of Network Data
statnet.org.
We have invested a lot of time and effort in creating the
statnet suite of packages for use by other researchers.
Please cite it in all papers where it is used.
For complete citation information, use
citation(package="statnet").
Creating User-defined terms using this package
The statnet suite of packages allows the
user to explore a large number of potential models
for their network data. These can be seen by typing
help("ergm-terms",package="ergm") (once the package
ergm has been installed). For more information on
the models and terms see Morris, Handcock, and Hunter (2008).
The purpose of the package is to allow additional terms can be coded up by users (you!) and be used at native speeds with statnet. So the suite of packages can be extended with minimal work by the user. In addition the core packages are not altered and so the new packages benefit from improvements to the core suite.
The process of creating new terms is explained in depth by the document entitled by Hunter, Goodreau, and Handcock (2010) that is found in the inst/doc directory of this package.
In brief, to add a new term you need to (all file references are relative to the package directory).:
1. Download the source version of this package. For example, in R use
download.packages("ergm.userterms", destdir=".", type="source")
where destdir is the directory to save the source
(e.g., ergm.userterms_3.0.tar.gz) in.
2. Unpack the source into a directory
(e.g., on Linux or the Mac
tar -vzxf ergm.userterms_3.0.tar.gz).
3. Optionally, rename the package name from
ergm.userterms to some something
more evocative of its use (e.g., myergm).
This can be done using a global change to the files in the
directory. Optionally, edit the DESCRIPTION file to reflect the
use of the package.
4. Edit R/InitErgmTerm.users.R to add
R InitErgmTerm functions for the new terms.
5. Edit src/changestats.users.c to add C
functions (like the example already in that file) to compute the
new change statistics.
6. Compile the package using the usual R
tools (e.g., R CMD build myergm and
R CMD INSTALL myergm).
7. Run it! It depends on ergm and network, of course. See the example below.
References
Hunter DR, Goodreau SM, Handcock MS (2013). ergm.userterms: A Template Package for Extending statnet, Journal of Statistical Software 52(2), 1-25, URL http://www.jstatsoft.org/v52/i02/.
See Also
statnet, network, ergm, ergm-terms
Examples
1 2 3 4 |
Example output
Loading required package: network
network: Classes for Relational Data
Version 1.13.0.1 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Martina Morris, University of Washington
Skye Bender-deMoll, University of Washington
For citation information, type citation("network").
Type help("network-package") to get started.
Loading required package: ergm
ergm: version 3.9.4, created on 2018-08-15
Copyright (c) 2018, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Martina Morris, University of Washington
with contributions from
Li Wang
Kirk Li, University of Washington
Skye Bender-deMoll, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm").
NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
constriant which distorted the sampled distribution somewhat. In
addition, Sampson's Monks datasets had mislabeled vertices. See the
NEWS and the documentation for more details.
Loading required package: statnet.common
Attaching package: 'statnet.common'
The following objects are masked from 'package:ergm':
colMeans.mcmc.list, sweep.mcmc.list
The following object is masked from 'package:base':
order
ergm.userterms: version 3.1.1, created on 2013-04-26
Copyright (c) 2013, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm.userterms").
NOTE: If you use custom ERGM terms based on 'ergm.userterms' version
prior to 3.1, you will need to perform a one-time update of the package
boilerplate files (the files that you did not write or modify) from
'ergm.userterms' 3.1 or later. See help('eut-upgrade') for
instructions.
Warning messages:
1: replacing previous import 'statnet.common::sweep.mcmc.list' by 'ergm::sweep.mcmc.list' when loading 'ergm.userterms'
2: replacing previous import 'statnet.common::colMeans.mcmc.list' by 'ergm::colMeans.mcmc.list' when loading 'ergm.userterms'
mindegree2
97
Starting maximum pseudolikelihood estimation (MPLE):
Evaluating the predictor and response matrix.
Maximizing the pseudolikelihood.
Finished MPLE.
Stopping at the initial estimate.
Evaluating log-likelihood at the estimate.
==========================
Summary of model fit
==========================
Formula: faux.mesa.high ~ mindegree(2)
Iterations: NA
Maximum Pseudolikelihood Results:
Estimate Std. Error MCMC % z value Pr(>|z|)
mindegree2 -4.8709 0.1294 0 -37.64 <1e-04 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning: The standard errors are based on naive pseudolikelihood and are suspect.
Null Pseudo-deviance: 28987 on 20910 degrees of freedom
Residual Pseudo-deviance: 17022 on 20909 degrees of freedom
AIC: 17024 BIC: 17031 (Smaller is better.)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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